No abstract available.
Brain Mapping* ; Evoked Potentials, Visual* ; Schizophrenia*

Radiogenomics or imaging genomics is a novel omics strategy of associating imaging data with genetic information, which has the potential to advance personalized medicine. Imaging features extracted from PET or PET/CT enable assessment of in vivo functional and physiological activity and provide comprehensive tumor information non-invasively. However, PET features are considered secondary to features on conventional imaging, and there has not yet been a review of the radiogenomic approach using PET features. This review article summarizes the current state of PET-based radiogenomic research for cancer, which discusses some of its limitations and directions for future study.

PURPOSE: The present study was designed to examine the distribution of dorsal root ganglion(DRG) cells innervating the anterior and posterior cruciate ligaments of the Sprague-Dawley rat knee joint. MATERIALS AND METHODS: Fluoro-gold(FG) was used to identify the distribution of DRG cells innervating the ligaments, and horseradish peroxidase(HRP) was used to measure the DRG cell size innervating the ligaments. RESULTS: Neural tracers-labelled DRG cells were found ipsilaterally only in the lumbosacra1 DRGs. FG-labelled DRG cells innervating the anterior and posterior cruciate ligaments were found from the 1st lumbar DRG to the 1st sacral DOR(L1-Sl). The majority of FG-labelled DRG cells innervating the poste-rior cruciate ligaments were located in the L4, and the majority innervating the anterior cruciate ligaments were found in the L3, The size of HRP-labelled DRG cells innervating the cruciate ligaments was below 800 micromiter (c), showing that these cells were small. CONCLUSION: This study indicates that the DRG origin of sensory nerves is different in each cruciate ligament of the knee joint. But the size and the type innervating each ligament is similar.
Animals ; Anterior Cruciate Ligament ; Armoracia ; Cell Size ; Diagnosis-Related Groups ; Horseradish Peroxidase ; Knee Joint* ; Knee* ; Ligaments ; Posterior Cruciate Ligament* ; Rats* ; Rats, Sprague-Dawley ; Spinal Nerve Roots*

No abstract available.
Neck* ; Thyroid Gland* ; Thyroid Neoplasms*

The rate of malignant transformation in porokeratosis (PK) lesions is approximately 7.5%, and linear PK demonstrates the highest rate of malignancy. An 83-year-old woman presented with a rapidly enlarging mass on her left arm. Variably sized erythematous scaly patches were scattered across the left half of her trunk and arm. Additionally, generalized variably sized brownish annular patches with a hyperkeratotic outer ring were observed on her face, trunk and bilateral arms. A skin biopsy was performed on 3 lesions-a yellowish to erythematous appearing mass, an erythematous scaly patch, and a brownish annular patch. Histopathological evaluation of these 3 lesions revealed squamous cell carcinoma, actinic keratosis, and PK, respectively. The final diagnosis was disseminated superficial PK with linear PK on the left side of the body, and actinic keratosis and squamous cell carcinoma confined to the linear PK lesions. We report a case which represents the progressive and stepwise malignant transformation of PK into squamous cell carcinoma.
Aged, 80 and over ; Arm ; Biopsy ; Carcinoma, Squamous Cell ; Diagnosis ; Female ; Humans ; Keratosis, Actinic ; Porokeratosis* ; Skin

No abstract available.
Aluminum* ; Onychomycosis* ; Yttrium*

BACKGROUND: Laser therapy can be used as an alternative treatment for onychomycosis; however, there are somelimitations to its efficacy as a single agent. OBJECTIVE: To evaluate the effectiveness of combination therapy with 1,064-nm neodymium-doped yttrium aluminum garnet (Nd:YAG) laser and topical efinaconazole in onychomycosis treatment and identify factors influencing the therapeutic outcomes of combination treatment. METHODS: Big toenails with onychomycosis were treated by 1,064-nm Nd:YAG laser at 4-week intervals with daily application of topical efinaconazole. Therapeutic response was assessed through onychomycosis severity index (OSI) and percentage of nail infected (PNI), and its association with a variety of factors that may affect treatment outcome was evaluated. RESULTS: One hundred big toenails were included in the study and significant clinical improvements were observed after treatment (OSI improvement score = 76.68 ± 28.83, PNI improvement score = 72.37 ± 30.37). There was no difference in treatment response according to the number of laser treatments, onychomycosis clinical type, or initial severity. However, patient age was negatively correlated with clinical improvement (p = 0.019). Also, female patients had better therapeutic responses than male patients. CONCLUSION Combined treatment with Nd:YAG laser and topical efinaconazole has a significant therapeutic effect on onychomycosis. A randomized controlled trial is warranted in the future.

PURPOSE: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models. METHODS: Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning. RESULTS: Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively. CONCLUSIONS ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician's diagnostic ability.

PURPOSE: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models.METHODS: Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning.RESULTS: Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively.CONCLUSIONS: ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician's diagnostic ability.
Classification ; Dataset ; Diagnosis ; Humans ; Lacrimal Apparatus Diseases ; Learning ; Logistic Models ; Machine Learning ; Nuclear Medicine ; Programming Languages ; Radionuclide Imaging